Hello,

I am using optical flow network to calculate flows between frames, but when the `nearest`

interpolation mode is adopted, it always return 0 grad back to optical flow network.

I know `bilinear`

mode is commonly adopted. But in my case `nearest`

sampling is necessary, so is there any solution making `nearest`

interpolation mode do not return 0 grad?

Thanks in advance!

I see that `nearest`

mode performs gradient backward correctly in the following example:

```
import torch, torch.nn.functionasl as F
x = torch.randn(1,1,5,5,requires_grad=True)
y = F.interpolate(x, size=(10,10), mode='nearest')
y.sum().backward()
print(x.grad)
```

Can you check if any other operation that you use breaks the computation graph or if the gradients are really `0`

from the later layers?